AI Stock Screener is an equity screening analyst that translates value, growth, GARP, dividend, and quality strategies into filters and evaluates survivors with quality signals, red flags, and peer context. This agent retains your thesis, sector thresholds, and prior shortlists instead of restarting every session. Built on Jenova's unified platform, it combines OpenAI, Anthropic, Google, and more with live search for current multiples and filings. This is financial research, not licensed investment advice, and it does not place trades.
Stock Screener converts a named style, a metric list, or a vague goal such as "I want stocks that will grow" into a short executable criteria set — usually four or five filters, not a twelve-factor maze. Deep value maps to P/E, P/B, EV/EBITDA, and price-to-free-cash-flow versus sector medians, with a watch for businesses that are cheap because they are shrinking. Growth maps to revenue and EPS trajectory and PEG; dividend income maps to yield, payout ratio, coverage, and streak length; quality compounders map to ROIC, margin stability, free-cash-flow conversion, and leverage.
Stock Screener starts with a wide quantitative net, then tightens on earnings quality, balance-sheet strength, and consistency, then removes accounting anomalies, clustered insider selling, litigation overhangs, and structural decline. If hundreds of names survive, the next move is a quality overlay — free-cash-flow conversion, three-year margin stability, leverage — not another point shaved off P/E. If nothing survives even relaxed criteria, the honest output is that the strategy may not fit current conditions, not a forced list of compromised names.
Stock Screener applies the ratios that fit the business and warns when a standard cutoff misleads. Banks are read on tangible book, ROE, net interest margin, and capital ratios such as CET1, not price-to-sales; REITs on FFO or AFFO, not GAAP earnings distorted by depreciation; SaaS on growth, net retention, and Rule of 40 long before a meaningful P/E exists; commodities on cycle-normalized earnings so peak profits do not look cheap. Cross-checks catch the rest: high ROE with weak ROIC is often leverage, revenue growing faster than cash collections can be channel stuffing, a high yield after a price collapse is a question, and rising earnings with a large accruals ratio can be accounting rather than cash.
Stock Screener returns structured assessments rather than a ranked ticker dump: company, ticker, sector, cap tier, fit versus your criteria, four to six strategy-relevant metrics, strengths, concerns, and a verdict — strong candidate, worth investigating, proceed with caution, or pass. Side-by-side tables come with a narrative on trade-offs, not a single winner that hides the risks. Current prices and ratios are pulled through search at query time; vendor disagreements and missing figures are stated instead of invented, and the assessed shortlist can be stored in Notion, Google Drive, or Dropbox so the next pass starts from named candidates.
| Traditional Approach | Stock Screener |
|---|---|
| Static filters dump a ticker list | Strategy translated into per-stock assessments |
| Absolute cutoffs ignore sector norms | Sector-relative thresholds plus peer context |
| Hours in EDGAR and spreadsheets | Live multi-source research with named gaps |
| Same ratios for banks and SaaS | Sector-specific metrics, misleading-ratio warnings |
Unlimited Memory vs. Chatbot Amnesia
A generic chatbot forgets the ROIC floor, the energy exclusion, and which industrials already failed the yield-trap pass the moment the thread ends. Stock Screener keeps the criteria set, the sector-relative thresholds, the names already labeled pass or proceed-with-caution, and the quality overlays you added after a 200-name dump. Without that continuity, the next session recasts the same wide net and you re-investigate value traps you already discarded.
Multi-Model AI Access
Turning "find quality compounders" into a four-filter spec is a reasoning job; checking three-year margin stability and accruals against peer medians is a different one; writing trade-off narratives across five survivors is a third. This agent can route those phases across OpenAI, Anthropic, Google, xAI, DeepSeek, Qwen, and more instead of locking you to a single vendor the way ChatGPT (OpenAI-only) or Claude (Anthropic-only) does. Custom model selection on paid tiers lets you pick the provider that fits the phase of the screen.
Intelligent Tool Integration for End-to-End Workflows
The production path is thesis → proposed filters → live market research → structured assessments → saved watchlist. Google Search pulls current multiples, filings, insider activity, and peer context at query time rather than from stale memory. Notion, Google Drive, or Dropbox hold the shortlist so the next session starts from assessed names, not a blank grid. That chain is a screening workflow, not a chat about stocks.
Sector Literacy Generic Assistants Do Not Carry
Generic models will happily run P/E under 15 across banks, REITs, and pre-profit software. Stock Screener treats that as a design error: it proposes P/TBV, ROE, and CET1 for a bank screen, FFO for a REIT, and Rule of 40 for SaaS, and it says so before the net is cast. The specialization is which metric belongs on which business, not a longer list of the same generic ratios.
Dividend-growth investors
Stock Screener runs a 2–5% yield band with a payout cap, free-cash-flow coverage, and consecutive-increase streak, then flags the industrial that only yields more because the price has already broken. Streak length is treated as evidence, not a guarantee.
Quality and value investors
This agent gates on ROIC, margin stability, free-cash-flow conversion, and leverage so levered financials and one-year wonders do not pass a "quality" screen on ROE alone. Valuation is a second question: survivors can be labeled worth investigating when the multiple already prices a perfect decade.
Investors who know the goal, not the spec
If you arrive with "U.S. large-cap value, cheap versus five-year averages, exclude energy," the AI proposes the criteria and explains why each one belongs. Interpreting payout ratios, ROIC versus ROE, or accruals is part of the job — U.S. adult financial literacy has hovered near 50% for eight consecutive years, and a blank filter grid assumes training most people never received.
After-hours and mobile researchers
A sector move after the close does not require a six-monitor desk. The same GARP rules, red-flag pass, and verdicts run on a phone, which matches how most banked adults already handle accounts.
Small-cap, momentum, and turnaround screeners
Multi-style and multi-factor screens are in scope, including small-cap value and turnaround theses. Geography should be stated when it is not obvious, because data quality and governance risk change the research path.
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Stock Screener is a research-focused AI that builds equity screens from a style or goal, then writes a per-name assessment covering screening fit, key metrics, concerns, and a verdict. It researches and compares stocks across global markets. It does not execute trades or construct your asset allocation.
Stock Screener keeps your criteria set, excluded sectors, and already-rejected tickers across sessions, so a yield trap flagged on Tuesday does not reappear as a fresh idea on Friday. It can switch among OpenAI, Anthropic, Google, xAI, DeepSeek, Qwen, and more when the job shifts from choosing bank metrics to comparing five survivors, instead of staying locked inside one vendor. Google Search, Notion, Google Drive, and Dropbox support the path from thesis to live research to a saved watchlist, which a standalone chatbot cannot run as a single workflow.
Stock Screener uses Google Search to pull current prices, ratios, filings, and peer multiples at the time you ask, then can store the assessed shortlist in Notion, Google Drive, or Dropbox. That path is thesis to live research to a persisted watchlist. Workspace tools on the same platform can carry a comparison memo out of the session when you are ready to share it.
Brokerage and web screeners are built to return every ticker that matches a numeric cutoff. Stock Screener is built for the steps around that cutoff: choosing criteria that fit the thesis, rejecting sector-inappropriate ratios, checking earnings quality, and explaining why a survivor is a candidate or a pass. Use a conventional screener when you only need a list. Use this agent when you need the list interpreted.
Yes. Dividend, deep value, GARP, growth, quality, momentum, small-cap value, and turnaround styles are all in scope, including multi-factor combinations. Geography should be specified when it is not obvious, because GAAP versus IFRS, data quality, and governance risk change the research path. Options, futures, forex, and crypto are outside this agent's scope.
No. Stock Screener produces financial research and analysis, not licensed investment advice, and it does not place trades. Past screening results do not guarantee future performance. For decisions that affect your finances, treat the output as a research memo and consult a qualified advisor.
Stock Screener refuses to apply the same P/E or price-to-sales cutoff across those businesses. Banks are evaluated on tangible book, ROE, net interest margin, and capital ratios; REITs on FFO or AFFO; SaaS on growth, net retention, and Rule of 40. If a requested metric is the wrong tool for the sector, you hear that before results are generated.
Too many names usually means add a quality overlay, not a tighter price target — quality removes noise, tighter multiples remove opportunity. Too few names means loosen the most restrictive filter first. If nothing survives even relaxed criteria, the AI will say the strategy may not fit current conditions rather than pad the list with compromised tickers.
Current prices, ratios, and ratings are pulled from search at the time you ask, not from memory, and sources are named inside the session. When data vendors disagree, the discrepancy is flagged. Missing figures are admitted instead of filled in.
Yes. Web, iOS, and Android share full parity, including speech-to-text if you would rather dictate a GARP screen than type one. A sector move after hours gets the same red-flag pass as a desktop session. Download Jenova on iOS or Android.
Screening criteria, shortlists, and conversation data are not used to train public AI models. Data is encrypted in transit and at rest and is not sold or shared with advertisers. Your thesis and rejected tickers stay in your workspace, not in someone else's training set.
Screening ends at a defensible watchlist, not a completed buy. Send a top pick to Fundamental Stock Analyst for earnings quality and filings, to Technical Stock Analyst for defined-risk timing, or to Portfolio Management Strategist when the question is what the name does to existing weights.
Stock Screener is part of Jenova, the most powerful AI agent platform in the world. Jenova provides unified access to leading AI models from OpenAI, Anthropic, Google, xAI, DeepSeek, Qwen, and more — alongside intelligent tool integrations for end-to-end task completion. The platform features persistent cross-session memory, automated workflow orchestration, and full parity across web, iOS, and Android. Developed by Azeroth Inc., a New York-based technology company, Jenova serves users in over 70 countries.
For developers, the Jenova Agent API provides a fully-managed agent backend with built-in orchestration, multi-model routing, memory, storage, and MCP tool support — replacing the need to stitch together raw LLM APIs, vector databases, and custom tool servers.
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